Description Usage Arguments Details Value Author(s) References Examples

View source: R/penLags-18-6-13.R

The function `penLags()`

fits a regression model to lags of an explanatory variable `x`

or to lags of `y`

itself.
The estimated coefficients of the lags are penalised using a quadratic penalty similar to P-splines.

1 2 3 |

`y` |
The response variable |

`x` |
The explanatory variable which can be the response itself if autoregressive model is required |

`lags` |
The number of lags required |

`from.lag` |
from which lag value to start, the default is zero which means include the original |

`weights` |
The prior weights |

`data` |
The data frame if needed |

`df` |
If not |

`lambda` |
If not |

`start.lambda` |
Staring values for lambda for the local ML estimation |

`order` |
The order of the penalties in the beta coefficients |

`plot` |
Whether to plot the data and the fitted values |

`method` |
The method of estimating the smoothing parameter with two alternatives, i) |

`k` |
The penalty required if the method |

`...` |
for further arguments |

This function is designed for fitting a simple penalised lag regression model to a response variable. The meaning of simple in this case is that only one explanatory variable can used (whether it is a true explanatory or the response variable itself) and only a normal assumption for the response is made. For multiple explanatory variables and for different distributions within `gamlss`

use the additive function `la`

.

Returns `penLags`

objects which has several method.

Mikis Stasinopoulos [email protected], Bob Rigby, Vlasios Voudouris, Majid Djennad, and Paul Eilers.

Benjamin M. A., Rigby R. A. and Stasinopoulos D.M. (2003) Generalised Autoregressive Moving Average Models. *J. Am. Statist. Ass.*, 98, 214-223.

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion),
*Appl. Statist.*, **54**, part 3, pp 507-554.

Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
*Journal of Statistical Software*, Vol. **23**, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/i07.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | ```
# generating data
y <- arima.sim(500, model=list(ar=c(.9,-.8)))
#----------------------------------
#fitting model with different order
m0 <- penLags(y,y, lag=20, order=0)
m1 <- penLags(y,y, lag=20, order=1)
m2 <- penLags(y,y, lag=20, order=2)
m3 <- penLags(y,y, lag=20, order=3)
# chosing the order
AIC(m0, m1, m2, m3)
#---------------------------------
# look at the AR coefficients of the models
op <- par(mfrow=c(2,2))
plot(coef(m0,"AR"), type="h")
plot(coef(m1, "AR"), type="h")
plot(coef(m2, "AR"), type="h")
plot(coef(m3,"AR"), type="h")
par(op)
#-------------------------------
# refit and plotting model
m1 <- penLags(y,y, lag=20, order=1, plot=TRUE)
# looking at the residuals
plot(resid(m1))
acf(resid(m1))
pacf(resid(m1))
# or better use plot, wp or dtop
plot(m1, ts=TRUE)
wp(m1)
dtop(m1)
# the coefficients
coef(m1)
coef(m1, "AR")
coef(m1, 'varComp')
#
print(m1)
#summary(m1)
# use prediction
plot(ts(c(y, predict(m1,100))))
``` |

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